SVM with Quadratic Polynomial Kernel Function Based Nonlinear Model One-step-ahead Predictive Control  

作　　者：钟伟民 何国龙 皮道映 孙优贤 

机构地区：[1]National Laboratory of Industrial Control TechnologyInstitute of Modern Control Engineering,Zhejiang University,Hangzhou 310027,China [2]Department of MathematicsZhejiang Normal University,Jinhua 321004,China

出　　处：《Chinese Journal of Chemical Engineering》2005年第3期373-379,共7页中国化学工程学报（英文版）

基　　金：Support by China 973 Project (No. 2002CB312200).

摘　　要：A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.A support vector machine (SVM) with quadratic polynomial kernel function based nonlinear model one-step-ahead predictive controller is presented. The SVM based predictive model is established with black-box identification method. By solving a cubic equation in the feature space, an explicit predictive control law is obtained through the predictive control mechanism. The effect of controller is demonstrated on a recognized benchmark problem and on the control of continuous-stirred tank reactor (CSTR). Simulation results show that SVM with quadratic polynomial kernel function based predictive controller can be well applied to nonlinear systems, with good performance in following reference trajectory as well as in disturbance-rejection.

关 键 词：nonlinear model predictive control support vector machine nonlinear system identification kernel function 

分 类 号：O231[理学—运筹学与控制论]